基于高分二号遥感数据的农业阳光大棚提取
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摘要
利用遥感技术及时、准确地获取大棚的空间分布和面积信息,对于农业结构调整、污染防治有着重要意义。以邯郸市馆陶县为研究区,基于国产高分二号(GF-2)卫星影像数据,利用面向对象的最近邻方法提取研究区农业阳光大棚信息。经随机点验证结果显示,大棚信息提取精度为95.65%,大棚面积为21.11 km~2。在空间上,馆陶县大棚沿交通干线以及重要河流形成了明显的聚集特征,尤以翟庄村附近为甚(2017年统计结果0.93 km~2)。鉴于官方统计的大棚面积同时包含纯大棚和附属设施面积,通过计算纯大棚面积占比对提取结果进行修订。修订后的大棚面积为33.68 km~2,相比于官方大棚面积统计数据(30 km~2),面积精度为87.80%。研究表明:面向对象的最近邻分类方法适用于在GF-2影像上快速提取温室大棚信息,并可以为研究区农业大棚空间规划与管理以及农业污染监管与防治提供技术支持。
Timely and accurately acquisition of the area and spatial distribution of greenhouse in the agricultural regions using remote sensing technique is a novel solution,which would be valuable for the local authorities taking measures to adjust regional agricultural structure and to prevent and control environmental pollution.In this study,the nearest neighbor method based on object-oriented thought is used to extract greenhouses in Guantao County of Handan City with GF-2 satellite image.The random verification shows that the accuracy of extraction in greenhouses is 95.65%,and the area of the greenhouse is 21.11 km~2.Since auxiliary facilities around greenhouses were also included in the area of greenhouses issued by local authority,the extraction results need to be revised by calculating the ratio of greenhouse in the greenhouse area.As a result,the final area of greenhouses is 33.68 km~2 with the area accuracy of 87.80%(compared with the official statistics:30 km~2).Greenhouses in Guantao County were obviously spatially clustered in some zones along traffic arteries and main rivers,especially around the Zhaizhuang village(about 0.93 km~2).Using Chinese high-resolution satellites images to extract information of greenhouses can be effective and feasible with suitable method,and can provide technical support for decision makers to the spatial planning and management of agricultural greenhouse and the supervision and control of agricultural pollution.
Timely and accurately acquisition of the area and spatial distribution of greenhouse in the agricultural regions using remote sensing technique is a novel solution,which would be valuable for the local authorities taking measures to adjust regional agricultural structure and to prevent and control environmental pollution.In this study,the nearest neighbor method based on object-oriented thought is used to extract greenhouses in Guantao County of Handan City with GF-2 satellite image.The random verification shows that the accuracy of extraction in greenhouses is 95.65%,and the area of the greenhouse is 21.11 km~2.Since auxiliary facilities around greenhouses were also included in the area of greenhouses issued by local authority,the extraction results need to be revised by calculating the ratio of greenhouse in the greenhouse area.As a result,the final area of greenhouses is 33.68 km~2 with the area accuracy of 87.80%(compared with the official statistics:30 km~2).Greenhouses in Guantao County were obviously spatially clustered in some zones along traffic arteries and main rivers,especially around the Zhaizhuang village(about 0.93 km~2).Using Chinese high-resolution satellites images to extract information of greenhouses can be effective and feasible with suitable method,and can provide technical support for decision makers to the spatial planning and management of agricultural greenhouse and the supervision and control of agricultural pollution.
引文
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[20] Guan Zhen,Cao Guangchao,Yi Junzhu.Research on Object-Oriented Classification of Remote Sensing Images[J].Science and Technology Innovation Herald,2010,(34):8-10.[管珍,曹广超,易俊柱.面向对象的遥感影像分类研究[J].科技创新导报,2010,(34):8-10.]
[21] Song Xiaoyang,Jiang Xiaosan,Jiang Dong,et al.Object-oriented Classification of High-resolution Remote Sensing Image[J].Remote Sensing Technology and Application,2015,30(1):99-105.[宋晓阳,姜小三,江东,等.基于面向对象的高分影像分类研究[J].遥感技术与应用,2015,30(1):99-105.]
[2] Min Ju,Lu Kouping,Lu Yufang,et al.Investigation of Soil Fertility and Quality of Ground Water in Greenhouse Vegetable Fields of Tai Lake Region[J].Soils,2012,44(2):213-217.[闵炬,陆扣萍,陆玉芳,等.太湖地区大棚菜地土壤养分与地下水水质调查[J].土壤,2012,44(2):213-217.]
[3] Li Zongnan,Jiang Yi,Wang Xin,et al.Remote Sensing Spatial-temporal Changes of Facility Planting Region in Luliang County[J].Chinese Journal of Agricultural Resources and Regional Planning,2018,39(12):21-25,33.[李宗南,蒋怡,王昕,等.基于遥感的陆良县设施种植区域时空变化监测[J].中国农业资源与区划,2018,39(12):21-25,33.]
[4] Li Jing,Zhao Gengxing,Li Tao,et al.Study on Technique of Extracting Greenhouse Vegetable Information from Landsat TM Image[J].Journal of Soil and Water Conservation,2004,18(1):126-129.[李静,赵庚星,李涛,等.TM影像中大棚菜地信息提取技术研究[J].水土保持学报,2004,18(1):126-129.]
[5] Chen Jun,Shen Runping,Li Bolun,et al.Development of Plastic Greenhouse Index based on Logistic Regression Analysis[J].Remote Sensing for Land and Resources,2019,31(3).doi:10.6046/gtzyyg.2019.03.06.[陈俊,沈润平,李博伦,等.基于Logistic回归分析的塑料大棚遥感指数构建研究[J].国土资源遥感,2019,31(3).doi:10.6046/gtzyyg.2019.03.06.]
[6] Huang Zhenguo,Chen Zhongxin,Liu Fangqing,et al.Monitoring of Greenhouse Vegetables Land Using HJ-1 Remotely-sensed Imagery[J].Chinese Journal of Agricultural Resources and Regional Pl-anning,2013,34(5):102-106.[黄振国,陈仲新,刘芳清,等.基于HJ-1影像的大棚菜地遥感监测技术研究—以山东寿光市为例[J].中国农业资源与区划,2013,34(5):102-106.]
[7] Wang Zhipan,Zhang Qingling,Qian Jing,et al.Greenhouse Extraction Based on the Enhanced Water Index-A Case Study in Jiangmen of Guangdong[J].Journal of Integration Technology,2017,6(2):11-21.[王志盼,张清凌,钱静,等.基于增强型水体指数的大棚遥感检测研究—以广东江门地区为例[J].集成技术,2017,6(2):11-21.]
[8] Wang Qitian,Lin Xiangguo,Wang Zhijun,et al.Extraction of Winter Wheat Planting Area by Object-oriented Classification Method[C]// Science of Surveying and Mapping,2008,33(2):143-146.[王启田,林祥国,王志军,等.利用面向对象分类方法提取冬小麦种植面积的研究[J].测绘科学,2008,33(2):143-146.]
[9] Coslu M,Sonmez N K,Koc-San D.Object-based Greenhouse Classification from High Resolution Satellite Imagery:A Case Study Antalya-Turkey[C]//International Society for Photogrammetry and Remote Sensing-International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2016,XLI-B7:183-187.
[10] Manuel A,Abderrahim N,Antonio N,et al.Object-based Greenhouse Mapping Using very High Resolution Satellite Data and Landsat 8 Time Series[J].Remote Sensing,2016,8(6):513.
[11] Gao Mengjie,Jiang Qunou,Zhao Yiyang,et al.Comparison of Plastic Greenhouse Extraction Method based on GF-2 Remote-sensing Imagery[J].Journal of China Agricultural University,2018,23(8):125-134.[高梦婕,姜群鸥,赵一阳,等.基于GF-2遥感影像的塑料大棚提取方法对比[J].中国农业大学学报,2018,23(8):125-134.]
[12] Hu Maoying.Research on the Object-oriented Method of Urban Building Extraction with GF-2 Remote-sensing Imagery[D].Jilin:Jilin University,2016.[胡茂莹.基于高分二号遥感影像面向对象的城市房屋信息提取方法研究[D].吉林:吉林大学,2016.]
[13] Lu Yunge,Fan Shuangliang,Li Chunlin.Application of GF-2 Satellite in Remote Sensing Monitoring on Mine Exploitation in Tibet[J].Spacecraft Recovery & Remote Sensing,2015,36(4):73-83.[路云阁,樊双亮,李春霖.“高分二号”卫星在西藏矿山遥感监测中的应用研究[J].航天返回与遥感,2015,36(4):73-83.]
[14] Zou Pu.Method Research for Geological Disaster Survey and Monitoring based on GF-2 Satellite Data Processing:Taking Hunan Xinhua Demonstration Zone as an Example[J].Geomatics & Spatial Information Technology,2017,40(11):20-22.[邹蒲.地质灾害遥感调查与监测中高分二号卫星数据应用研究—以湖南新化示范区为例[J].测绘与空间地理信息,2017,40(11):20-22.]
[15] Lan Zhangren,Zhang Dongshui.Remote Sensed Information Extracting Model based on Multiple-objective Decision Support Theory:A Case Study of Wetland Agriculture[J].Transactions of the Chinese Society Agricultural Engineering,2008,24(7):155-159.[兰樟仁,张东水.遥感影像多目标优化信息提取模式研究[J].农业工程学报,2008,24(7):155-159.]
[16] Lang R,Shao G,Pijanowski B C,et al.Optimizing Unsupervised Classifications of Remotely Sensed Imagery with a Data-assisted Labeling Approach[J].Computers & Geosciences,2008,34(12):1877-1885.
[17] Zhao Chunxia,Qian Lexiang.Comparative Study of Supervised and Unsupervised Classification in Remote Sensing Image[J].Journal of Henan University:Natural Science,2004,34(3):90-93.[赵春霞,钱乐祥.遥感影像监督分类与非监督分类的比较[J].河南大学学报:自然版,2004,34(3):90-93.]
[18] Wei Yunchun,Tang Guoan,Wang Min,et al.Remote Sensing Digital Image Processing Tutorial.Version2[M].Beijing:Science Press,2015.[韦玉春,汤国安,汪闽,等.遥感数字图像处理教程.第2版[M].北京:科学出版社,2015.]
[19] Du Fenglan,Tian Qingjiu,Xia Xueqi,et al.Object-oriented Image Classification Analysis and Evaluation[J].Remote Sensing Technology and Application,2004,19(1):20-23.[杜凤兰,田庆久,夏学齐,等.面向对象的地物分类法分析与评价[J].遥感技术与应用,2004,19(1):20-23.]
[20] Guan Zhen,Cao Guangchao,Yi Junzhu.Research on Object-Oriented Classification of Remote Sensing Images[J].Science and Technology Innovation Herald,2010,(34):8-10.[管珍,曹广超,易俊柱.面向对象的遥感影像分类研究[J].科技创新导报,2010,(34):8-10.]
[21] Song Xiaoyang,Jiang Xiaosan,Jiang Dong,et al.Object-oriented Classification of High-resolution Remote Sensing Image[J].Remote Sensing Technology and Application,2015,30(1):99-105.[宋晓阳,姜小三,江东,等.基于面向对象的高分影像分类研究[J].遥感技术与应用,2015,30(1):99-105.]